Quality, Not Speed: Building a Production Evaluation Framework for AI-Assisted Medical Document Authoring

Pharmaceutical medical information teams are responsible for responding to clinical inquiries from healthcare professionals about marketed drugs, using sources like approved literature and internal data. A bottleneck in this process isn't the writing, which is straightforward for trained medical writers, but rather locating, citing, and verifying the relevant source material. The introduction of an AI-assisted authoring platform aims to streamline this process by focusing on continuous quality measurement rather than speed, as regulatory compliance and accuracy are paramount. The platform is structured around a three-layer measurement system that evaluates AI-generated content before any human edits, emphasizing quality over speed to manage risks associated with regulatory compliance. The evaluation framework operates with a Composite Quality Index (CQI) that measures output based on clinical risks, giving more weight to omissions than inclusions. The system's architecture ensures that every claim in a document is traceable to a specific source, reducing the risk of ungrounded claims and supporting regulatory requirements. While the platform shows promising improvements in document quality and throughput, limitations such as cohort size, cross-tenant calibration, and potential judge drift remain, necessitating further development and a larger sample size for stronger claims of generalizability.

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